One of the current features supported by many modern software systems is the ability to store and play audio files. Many of these systems enable users to store and manage differing collections of audio files. As time passes however, many users become frustrated with the large amount of data that inevitably takes up larger amounts of storage space. Also, as collections become larger, it becomes more difficult and time consuming to retrieve and play desired audio information. Many systems offer software to help users manage these ever increasing volumes of audio information. For example, these systems may include an audio manager that supports popular audio file formats, including MP3, Ogg Vorbis (OGG), Windows Media Audio (WMA), MPC and MP+ files, and so forth. This enables users to catalog their entire collection of audio files and instantly search for favorite songs, browsing albums using the album cover as a thumbnail, creating reports and other useful features.
In addition to organizing audio files, these systems provide a set of tools to manage files by editing tags, changing names, editing lyrics, creating CDs, and looking up artist information, for example. Users can work with audio files stored on hard discs, CD-ROMs, network drives, ZIP drives or any other removable media. This includes tools that allow users to play multiple play lists and display images associated with each title. Additional features include automatically generated database statistics, personal ratings, sorting by genre, mood, year, and custom database queries.
Audio fingerprinting (AFP) has recently emerged as a powerful method for identifying audio, either in streams or in files. Several companies now offer music services based on audio fingerprinting. These services require that one or more fingerprints be extracted from the audio to be identified, and that these fingerprints be checked against a large database of previously-computed fingerprints.
Managing large audio collections is difficult, however, since it's not currently possible to quickly parse audio files (as opposed to images, for which thumbnails can be used). Users generally must rely on labeling, but even that is of limited help: often the labeling is inaccurate, but even with accurate labeling, users may not remember a given song until they hear it. If a user can't remember what a song sounds like, they usually must play it, and then stop play when they have recognized the music. In addition, some scenarios require a ‘hands-off’ approach to music selection: for example, one may desire to browse an audio collection, while driving, to select a song.
Previous efforts have attempted to summarize music to alleviate the problem of music browsing. However, these previous efforts have concentrated on computing features from single frames of audio. These frames are typically 16-30 milliseconds long. The previous efforts compute similarity between such frames. This similarity will necessarily be crude, due to the inadequate information available to the similarity metric.